Nishimoto Naoki, Terae Satoshi, Uesugi Masahito, Tanikawa Takumi, Endou Akira, Ogasawara Katsuhiko, Sakurai Tsunetaro
Department of Medical Informatics, Graduate School of Medicine, Hokkaido University, Sapporo, Japan.
AMIA Annu Symp Proc. 2008 Nov 6:1070.
The purpose of this study is to develop a module for correcting errors in the product of a natural language parser. When tested with 300 CT reports, a total of 604 patterns were generated. The recall and precision was improved to 90.7% and 74.1% after processed by the module from initial 80.5% and 42.8% respectively. This rule-based module will help health care personnel reduce the cost of manual tagging correction for corpus building.
本研究的目的是开发一个用于纠正自然语言解析器产品中错误的模块。在用300份CT报告进行测试时，总共生成了604个模式。经该模块处理后，召回率和精确率分别从最初的80.5%和42.8%提高到了90.7%和74.1%。这个基于规则的模块将帮助医护人员降低用于语料库构建的人工标注校正成本。
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